Research On Vehicle Obstacle Detection And Lane Change Decision Of Self-driving Car

With the development of society and economy,the concept of unmanned driving has gradually entered into daily life.Compared with manned driving systems,unmanned driving can avoid traffic accidents caused by drivers' inattention and can liberate drivers.People are full of longing for the convenience and safety brought by unmanned driving while it has attracted the attention of researchers,but there are still many challenges in the actual landing of unmanned driving.The main problems to be solved include the accuracy of obstacle target detection in environmental perception,while the accuracy and safety of autonomous vehicle decision-making are also waiting to be solved.In this paper,the problems of low accuracy and slow speed of vehicle obstacle detection in the unmanned environment perception technology are specifically improved,while we build a model to analyze the decision-making behavior of vehicle's free lane change.Aiming at the problems of low clustering accuracy and multiple hyperparameters in the traditional rasterized vehicle target detection method based on lidar,a density peaks clustering algorithm based on mutual neighbors with adaptive merge strategy(MNN-ADPC)is proposed based on the existing fast density peaks clustering algorithm(DPC);Aiming at the problems of insufficient point cloud feature extraction and unreasonable loss constraints in the lidar-based neural network vehicle target detection method,an improved 3D target detection network that optimizes the feature extraction module and loss terms is proposed;Aiming at the problem that vehicle's free lane changing behavior is difficult to predict accurately due to many affected factors,the gradient boosting decision tree is applied to perform feature transformation.In combination with logistic regression,a fusion decision model is constructed to analyze the vehicle's free lane changing behavior.The main research work and results of this article are as follows1)In the traditional rasterized vehicle target detection method based on lidar,the traditional clustering method used in obstacle grid clustering has problems such as low clustering accuracy,slow speed and too many hyperparameters.These problems severely affected the accuracy and adaptability of the clustering algorithm.Through the improvement of the initial clustering center selection and the division of points to be allocated in the existing fast density peaks clustering algorithms(DPC),density peaks clustering algorithm based on mutual neighbors with adaptive merge strategy(MNN-ADPC)was proposed.The improved clustering algorithm adopts a looser initial clustering center selection method,and combines subsequent adaptive merge strategy to solve the problem of cluster center missing selection in fixed threshold screening method.Aiming at the misclassification of the nearest neighbor partitioning strategy in original DPC,the improved clustering algorithm uses a more robust mutual neighbor partitioning strategy,which solves the problem of misclassification in embedded data.The comparative experiments results on multiple data sets show that the proposed MNN-ADPC has better accuracy and adaptability.At the same time,compared with the original DPC,the improved clustering algorithm has fewer hyperparameters,and does not require human involvement.In a real unmanned environment,the MNN-ADPC can obtain more accurate clustering results.2)Aiming at the existing lidar-based neural network vehicle target detection methods,the 3D vehicle target detection network is affected by the sparse characteristics of the point cloud,and there are major problems in the extraction of point cloud features.We improved the existing 3D object detection network based on lidar by optimizing the feature extraction module and loss function in the original detection network to obtain better detection results and an improved 3D vehicle target detection network is proposed.Aiming at the problem that different lidar points have different levels of contribution in feature extraction in the original detection network,feature weight learning strategies are added to the improved detection network to obtain more distinguished effective features.In addition,for the problems of unreasonable vehicle angle constraints in the original detection network,by adding a sine function transformation,the impact of the unreasonable vehicle angle loss on the model detection effect is solved.The comparative experiment result on the public data set KITTI shows the accuracy and effectiveness of the proposed improved 3D vehicle target detection network based on lidar.Compared with the original detection network,a higher average detection accuracy is obtained.3)Aiming at the problem that vehicle's free lane changing behavior is difficult to predict accurately due to many affected factors,the gradient boosting decision tree is applied to perform feature transformation.In combination with logistic regression,a fusion decision model is constructed to analyze the vehicle's free lane changing behavior.On the real vehicle trajectory dataset NGSIM,the vehicle free lane change behavior dataset is built with data filtering and screening.And through in-depth analysis of the vehicle's lane changing decision behavior process,more effective lane change decision variables are mined to further improve the prediction accuracy of the lane change decision model.Through comparative experiments,the results show that the fusion decision model proposed in this paper can obtain higher prediction accuracy than other single models.In addition,the newly extracted collision time feature is considered to be the most influential under various evaluation indicators.The characteristics prove the effectiveness of our lane change decision variables construction.